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Record W3195342422 · doi:10.1139/cjce-2020-0599

Empirical correlation of the modified wheel tracker (MWT) and the dynamic creep test for evaluating the permanent deformation of hot mix asphalt (HMA)

2021· article· en· W3195342422 on OpenAlexvenueno aff
Abhirup B. Roy-Chowdhury, Mofreh Saleh, Miguel Moyers-Gonzalez

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsCreepDeformation (meteorology)Materials scienceRutShear (geology)AsphaltDirect shear testTracking (education)Structural engineeringRepeatabilityTest dataGeotechnical engineeringComposite materialGeologyComputer scienceEngineeringMathematics

Abstract

fetched live from OpenAlex

The wheel tracking test (WTT) has been extensively used for laboratory characterization of permanent deformation of hot mix asphalt (HMA). However, the fully-confined setup of the device is unable to capture the tertiary zone where shear deformation takes place. This makes the permanent deformation resistance characteristics of HMA difficult to analyze. Hence, a modified wheel tracker (MWT), with unconfined lateral sides along the wheel tracking direction was utilized in this study, which can capture the tertiary flow (referred to as the flow number (FN)). The dynamic creep test, which yields FN, is a common laboratory test which has the ability to characterize permanent deformation considering the shear behavior. The study investigated the relationship of the MWT and the dynamic creep test utilizing the permanent deformation and permanent microstrain data from the respective tests. A novel parameter, FN-Index was explored. The permanent deformation parameters derived from the MWT were well correlated with the dynamic creep test. The MWT showed promising repeatability for the FNs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.314

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.257
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2021
Admission routes1
Has abstractyes

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